{"id":36443,"date":"2018-11-14T06:11:13","date_gmt":"2018-11-14T11:11:13","guid":{"rendered":"https:\/\/digital.hbs.edu\/platform-rctom\/submission\/spotify-is-better-at-picking-your-music-than-you-are-but-will-they-always-be-the-best\/"},"modified":"2018-11-14T06:11:13","modified_gmt":"2018-11-14T11:11:13","slug":"spotify-is-better-at-picking-your-music-than-you-are-but-will-they-always-be-the-best","status":"publish","type":"hck-submission","link":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/spotify-is-better-at-picking-your-music-than-you-are-but-will-they-always-be-the-best\/","title":{"rendered":"Spotify is better at picking your music than you are, but will they always be the best?"},"content":{"rendered":"

Music streaming services today largely rely on a model of providing an extensive catalog of music of roughly 30M tracks for ad-free listening for about $10 monthly.[1]<\/a> Thus, product differentiation is difficult to achieve, threatening the competitive positioning and pricing power of players in the industry.[2]<\/a> To combat this, companies are implementing machine learning to add elements of personalization to increase their value proposition to customers and artists. Spotify, launched in 2006 and now the leader in the space with more than 35M+ paid users and 100M active users across 58 countries, has achieved its success thus far through best-of-kind execution of machine learning to develop personalized product features, such as Discover Weekly, that enable the product to deeply connect with its customers.[3]<\/a>\u00a0 But the key question remains, can this competitive advantage last?<\/p>\n

\"\"<\/a><\/p>\n

\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 Source: Vidhya Murali, \u201cMusic Personalization at Spotify,\u201d Power point presentation, September 15-19, 2016.<\/h6>\n

Whereas its competitors rely on a singular application of machine learning, Spotify has combined three predominant machine learning strategies in order to create its more satisfying discovery engine that powers the company\u2019s product development (see above graphic).[4]<\/a>[5]<\/a>[6]<\/a> First, Spotify utilizes collaborative filtering, leveraging implicit feedback, such as the stream counts of tracks, to develop a profile for each of its 100M+ users.[7]<\/a>[8]<\/a> \u00a0Next to understand the music component, Spotify relies on Natural Language Processing to pull text from blogs, news articles, et cetera, to understand what words and adjectives are associated with songs and artists.[9]<\/a>\u00a0 The final component is the utilization of audio models to analyze raw audio tracks, studying aspects such as key, tempo, and volume, enabling Spotify to understand the fundamental similarities between songs.[10]<\/a> The user-specific data is captured in the rows of Spotify\u2019s vector system, with each row representing a user\u2019s preferences, while these latter two components are captured in columns, representing song-specific information (see graphic below).[11]<\/a> Through analyzing this user-song vector, Spotify can understand which users are similar to other users, as well as which songs are similar to other songs, and therefore match fitting songs and users across the platform.[12]<\/a> With this system in place, Spotify can deliver highly effective customized music curation through its Discover Weekly playlist function, which has earned the company overwhelmingly positive acclaim from customers and consequently, driven growth and retention.[13]<\/a><\/p>\n

\"\"<\/a><\/p>\n

\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0Source: Vidhya Murali, \u201cMusic Personalization at Spotify,\u201d Power point presentation, September 15-19, 2016.<\/h6>\n

Though Spotify has been successful thus far, continuous improvement with regards to its employment of machine learning techniques and applications is a priority amongst management, as the company seeks to maintain its competitive edge. One such near-term focus is to improve the quality and variety of content that informs the music-specific component of the analysis, which will further enhance the quality of the personalized playlists. Specifically, Spotify seeks to add analysis of lyrics and album-related images to their song and artist categorization.[14]<\/a> A longer-term focus is to further \u2018personalize\u2019 the model, rather than relying on the existing technique of bucketing by \u2018similarities.\u2019[15]<\/a> The closer Spotify can get to achieving true personalization, the more difficult it will be for competitors to close the product-quality gap. Another medium-term initiative is the expansion of its catalog into shows and podcasts[16]<\/a>; management sees opportunity to leverage its existing machine learning platform to create similar value in the content curation of additional media forms.<\/p>\n

There are further opportunities for management to extract value from its machine learning capabilities to further differentiate its product and maintain its leadership position in the music streaming space. In the near term, Spotify can leverage its deep understanding of its customers\u2019 music preferences to help artists in the content creation process, by way of selling analysis to artists to provide a clearer understanding of the qualities and fundamental components that lead to song success amongst various listener groups. Because Spotify is a network, it is critical to provide value-additive offerings to both sides of the equation, the artists and listeners, in order to strengthen the network effect. Though it would be difficult for Spotify to succeed in the actual content creation on the music side of the business, over the medium-term, I see an opportunity for Spotify to leverage its deep knowledge of customers and their preferences to create its own content on the podcast and radio show front. As we\u2019ve seen from Netflix, the creation of proprietary content is an effective method to increase competitive advantage via product differentiation, and thus reduce customer switching between platforms.<\/p>\n

Though Spotify has been successful in its pursuit of differentiation via leveraging machine learning up to this point, the question of whether this advantage is sustainable is a key one. Will there always be opportunities for Spotify to further advance their algorithms and data quality, or is it inevitable that competition eventually catches up?[17]<\/a> In terms of overall technological disruption, is there a world in which music streaming becomes obsolete or is music streaming driven by machine learning the final frontier for music consumption? (788)<\/p>\n

[1]<\/a> Trefis Team, \u201cSpotify Has Seen A Big Rally, But Still Faces Some Challenges,\u201d Forbes,<\/em> June 27, 2018, <\/em>https:\/\/www.forbes.com\/sites\/greatspeculations\/2018\/06\/27\/spotify-has-seen-a-big-rally-but-still-faces-some-challenges\/#4a38e97d3ffb<\/a>, accessed November 2018.<\/p>\n

[2]<\/a> Trefis Team, \u201cSpotify Has Seen A Big Rally, But Still Faces Some Challenges,\u201d Forbes,<\/em> June 27, 2018, <\/em>https:\/\/www.forbes.com\/sites\/greatspeculations\/2018\/06\/27\/spotify-has-seen-a-big-rally-but-still-faces-some-challenges\/#4a38e97d3ffb<\/a>, accessed November 2018.<\/p>\n

[3]<\/a> Vidhya Murali, \u201cMusic Personalization at Spotify,\u201d Power point presentation, September 15-19, 2016, https:\/\/www.slideshare.net\/vidhyamurali\/music-personalization-at-spotify<\/a>, accessed November 2018.<\/p>\n

[4]<\/a> Ying Qin, \u201cA Historical survey of Music Recommendation Systems: Towards Evaluation,\u201d Music Technology Area, Department of Music Research, Schulich School of Music, McGIll University, Montreal, Canada (April 2013). http:\/\/digitool.library.mcgill.ca\/webclient\/StreamGate?folder_id=0&dvs=1542151925966~558<\/a>, accessed November 2018.<\/p>\n

[5]<\/a> \u201cHow does Spotify Know You So Well,\u201d A Medium Corporation, October 10, 2017, https:\/\/medium.com\/s\/story\/spotifys-discover-weekly-how-machine-learning-finds-your-new-music-19a41ab76efe<\/a>, accessed November 2017.<\/p>\n

[6]<\/a> Nicholas Silipo, \u201cRecommended For You: <\/em>The Use of Machine Learning and Prediction Engines in Music Apps,\u201d CUNY Hunter GSR, (Spring 2018).<\/p>\n

[7]<\/a> Vidhya Murali, \u201cMusic Personalization at Spotify,\u201d Power point presentation, September 15-19, 2016, https:\/\/www.slideshare.net\/vidhyamurali\/music-personalization-at-spotify<\/a>, accessed November 2018.<\/p>\n

[8]<\/a> Nicholas Silipo, \u201cRecommended For You: <\/em>The Use of Machine Learning and Prediction Engines in Music Apps,\u201d CUNY Hunter GSR, (Spring 2018).<\/p>\n

[9]<\/a> Vidhya Murali, \u201cMusic Personalization at Spotify,\u201d Power point presentation, September 15-19, 2016, https:\/\/www.slideshare.net\/vidhyamurali\/music-personalization-at-spotify<\/a>, accessed November 2018.<\/p>\n

[10]<\/a> Vidhya Murali, \u201cMusic Personalization at Spotify,\u201d Power point presentation, September 15-19, 2016, https:\/\/www.slideshare.net\/vidhyamurali\/music-personalization-at-spotify<\/a>, accessed November 2018.<\/p>\n

[11]<\/a> How does Spotify Know You So Well,\u201d A Medium Corporation, October 10, 2017, https:\/\/medium.com\/s\/story\/spotifys-discover-weekly-how-machine-learning-finds-your-new-music-19a41ab76efe<\/a>, accessed November 2017.<\/p>\n

[12]<\/a> Vidhya Murali, \u201cMusic Personalization at Spotify,\u201d Power point presentation, September 15-19, 2016, https:\/\/www.slideshare.net\/vidhyamurali\/music-personalization-at-spotify<\/a>, accessed November 2018.<\/p>\n

[13]<\/a> Nicholas Silipo, \u201cRecommended For You: <\/em>The Use of Machine Learning and Prediction Engines in Music Apps,\u201d CUNY Hunter GSR, (Spring 2018).<\/p>\n

[14]<\/a> Vidhya Murali, \u201cMusic Personalization at Spotify,\u201d Power point presentation, September 15-19, 2016, https:\/\/www.slideshare.net\/vidhyamurali\/music-personalization-at-spotify<\/a>, accessed November 2018.<\/p>\n

[15]<\/a> Vidhya Murali, \u201cMusic Personalization at Spotify,\u201d Power point presentation, September 15-19, 2016, https:\/\/www.slideshare.net\/vidhyamurali\/music-personalization-at-spotify<\/a>, accessed November 2018.<\/p>\n

[16]<\/a> Vidhya Murali, \u201cMusic Personalization at Spotify,\u201d Power point presentation, September 15-19, 2016, https:\/\/www.slideshare.net\/vidhyamurali\/music-personalization-at-spotify<\/a>, accessed November 2018.<\/p>\n

[17]<\/a> E. Brynjolfsson and A. McAfee. \u201cWhat\u2019s driving the machine learning explosion?\u201d <\/em>性视界 Business Review Digital Articles (Jul 19, 2017.)<\/p>\n","protected":false},"excerpt":{"rendered":"

Machine learning at Spotify is a source of product differentiation in the music streaming industry.<\/p>\n","protected":false},"author":11380,"featured_media":36828,"comment_status":"open","ping_status":"closed","template":"","categories":[1067,2641,2606,346,249,688,5128,2738,2484],"class_list":["post-36443","hck-submission","type-hck-submission","status-publish","has-post-thumbnail","hentry","category-apple","category-digital-streaming","category-livemusic","category-machine-learning","category-music","category-pandora","category-playlists","category-spotify","category-streaming-services","hck-taxonomy-organization-spotify","hck-taxonomy-industry-music","hck-taxonomy-country-united-states"],"connected_submission_link":"https:\/\/d3.harvard.edu\/platform-rctom\/assignment\/rc-tom-challenge-2018\/","yoast_head":"\nSpotify is better at picking your music than you are, but will they always be the best? - Technology and Operations Management<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/spotify-is-better-at-picking-your-music-than-you-are-but-will-they-always-be-the-best\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Spotify is better at picking your music than you are, but will they always be the best? 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